1. Field of the Invention
The present invention relates to an apparatus and a method for detecting a decrease in a tire air pressure of a running vehicle based on the resonance frequency of the tire as well as a program for detecting a decrease in a tire air pressure.
2. Description of the Related Art
Factors for enabling the safe running of an automobile include a tire air pressure. If the air pressure value decreases and is lower than an appropriate value, this may cause deteriorated operation stability and fuel efficiency to consequently cause a tire burst. To prevent this, a tire air pressure alarming apparatus (Tire Pressure Monitoring System; TPMS), which is to detect the decrease in a tire air pressure to notify a driver of this decrease to prompt the driver to take an appropriate measure, is an important technique from the viewpoints of environment conservation and the secured safety of the driver.
A conventional alarming apparatus can be classified into two types of the direct detecting-type one (direct TPMS) and the indirect detecting-type one (indirect TPMS). The direct TPMS directly measures a tire air pressure by providing a pressure sensor in a tire wheel. The direct TPMS can accurately detect a decrease in a tire air pressure but requires an exclusive wheel and has a poor fault-tolerance performance in an actual environment for example. Thus, the direct TPMS has disadvantages in technique and cost.
On the other hand, the indirect TPMS uses a method to estimate an air pressure based on the rotation information of a tire. The indirect TPMS can be further classified into the dynamic loaded radius (DLR)-type one and the resonance frequency mechanism (RFM)-type one. The DLR method uses a phenomenon according to which a tire having a reduced pressure collapses during running to cause a reduced dynamic loaded radius and this tire is consequently rotated at a speed higher than that of a tire having a normal pressure. The DLR method compares rotation velocities of four tires to thereby detect a pressure decrease. Since the DLR method can provide a relatively-easy computation using only a wheel rotation velocity signal obtained from a wheel speed sensor, the DLR method has been widely researched mainly for the purpose of detecting the puncture of one wheel. However, this DLR method merely makes a relative comparison among wheel rotation velocities and thus cannot sense a case of four tire simultaneous deflation (natural leakage). Furthermore, this DLR method also has a disadvantage in that this method cannot accurately sense a reduced pressure in all vehicle running statuses because a difference in the wheel speed is also caused by running conditions such as a vehicle turning, acceleration and deceleration, or an uneven load.
On the other hand, the RFM method is a method that uses a fact that a frequency characteristic of a wheel speed signal changes depending on a reduced tire pressure to thereby detect a difference between a reduced tire pressure and a normal tire pressure. In contrast with the DLR method, the RFM method is based on an absolute comparison between a certain value and the normal values of the respective wheels retained in advance. Thus, the RFM method can cope with a case of four tire simultaneous deflation and the RFM has collected attention as a better indirect detecting method. However, the RFM method is disadvantageous in that some running condition causes a strong noise for example and thus an estimated frequency value in a target domain is not robust enough with regard to a vehicle speed or a road surface condition for example. The present invention relates to a tire status sensing apparatus based on the RFM method. The following section will describe the basic principle of the RFM method in more detail.
When a vehicle is running, the torsional motion in the front-and-rear direction caused by the force to a tire from the road surface and the front-and-rear motion of the suspension have a coupled resonance. This resonance phenomenon also has an influence on the wheel rotation motion. Thus, a wheel speed signal obtained from a wheel sensor provided in an anti-lock braking system (ABS) also includes the information regarding the resonance phenomenon. The coupled resonance is based on a unique vibration mode caused by the torsional rigidity of a tire. Thus, the excitation status thereof changes depending only on a change in the air pressure constituting the physical characteristic of the tire and rarely depends on a change in the vehicle speed or the road surface. Specifically, when the air pressure decreases, the dynamics of the torsional motion of the tire changes. Thus, when the wheel speed signal is subjected to a frequency analysis for a case where a tire has a reduced pressure, a peak shown by the coupled resonance (resonance peak) appears at a lower frequency-side than in the case where the tire has a normal pressure.
FIG. 8 illustrates the power spectrum obtained by subjecting the respective wheel acceleration signals obtained during a fixed time (which are obtained by calculating time differences among wheel speed signals) to Fast Fourier Transform (FFT) regarding tires attached to a vehicle having a normal air pressure and tires having a pressure reduced by 25% from the normal pressure.
The components in the vicinity of 40 to 50 Hz show the vibration caused when the vibration of the tires in the front-and-rear direction is resonant with the suspension of the vehicle. As can be seen from the components, a change in the internal pressure causes a frequency having a peak value (resonance frequency) to move to the lower-frequency-side. Due to the above-described characteristic, this phenomenon appears independently from the tires, the vehicle type, the running speed, and the road surface condition for example. Thus, this RFM method focuses on this resonance frequency and issues an alarm when it is determined that the frequency is relatively lower than a reference frequency estimated during initialization. In this case, the resonance frequency must be estimated based on a wheel speed signal obtained from ABS. However, an in-vehicle calculator having a limited computational resource has a difficulty in storing required time-series data, thus making it difficult to carry out the FFT frequency analysis. Due to this reason, a conventional method estimates the resonance frequency based on an on-line method as will be described later.
Since vibration can be described by the 2-order model, a wheel speed signal is subjected to a time-series analysis based on the 2-order autoregressive (AR) model. Specifically, a parameter θ={a1, . . . , aK} in the model represented by the following formula (1) is estimated by the Kalman filter (iterative least squares technique).
                              y          ⁡                      (            t            )                          =                                            ∑                              i                =                1                            K                        ⁢                                          a                i                            ⁢                              y                ⁡                                  (                                      t                    -                    i                                    )                                                              +          ɛ                                    (        1        )            
In this formula, y(t) represents a wheel speed at the time t, ε represents white noise, and K represents the model order. Since the frequency corresponding to the pole of the transfer function representing the AR model is estimated as a resonance frequency, the resonance frequency can be accurately obtained if a resonance peak is correctly extracted by the model.
However, many conventionally-suggested methods have disadvantages as described below. First, when a vehicle is running on a road having a smooth surface, the tires receive a weak force from the road surface and thus the resonance phenomenon is reduced. Consequently, noise mixed independently from a target signal has a relatively-increased influence. Specifically, a signal noise (SN) ratio is consequently poor and thus an accurate estimation of the resonance frequency is difficult. Secondly, when the vehicle is running at a high speed (80 km or more per hour), causing factors such as an increase in the up-and-down vibration of the tires cause a stronger signal having a high frequency component, thus causing a tendency where the resonance peak is unclear. Thirdly, when a strong noise has a peak in the vicinity of a frequency domain having a resonance peak, the influence by this noise causes an unstable estimated resonance frequency value. FIG. 9 shows the result obtained by applying FFT to the running data obtained under the circumstance as described above. Some running condition causes these problems to be superimposed, thus making it particularly difficult to estimate a resonance frequency in a stable manner. Furthermore, an unstable estimated value also makes it difficult to set a reference value for determining abnormality, which is a causing factor that makes it difficult to put an abnormality detection system based on the RFM method into practical use.
As a method for solving the disadvantage of the RFM method as described above, a method has been disclosed in Japanese Unexamined Patent Publication No. 2005-14664. This method performs initializations and estimates by the number of combinations of causing factors having an influence on the estimation of the air pressure (i.e., road surface, vehicle speed, and temperature). The specification of Japanese Patent No. 3152151 discloses that external factors having an influence on the resonance frequency are three factors of an outside temperature, a vehicle speed, and the vibration from the road surface to a tire. By using a correction function determined in advance based on any two or all of these three external factors, an estimated value is corrected depending on the circumstance. However, in the case of the method disclosed in Japanese Unexamined Patent Publication No. 2005-14664, the total of 27 windows are required when three types are assumed for the respective causing factors. Initialization of all of these 27 windows is not only complicated but also causes a case where a not-yet-initialized window cannot be subjected to the determination of abnormality. Another method also has been suggested according to which a not-initialized window is interpolated by the interpolation and extrapolation based on surrounding windows. However, since the interpolation method is not based on a tire physical characteristic, the abnormality detection accuracy has a poor reliability.
Furthermore, the correction function according to the specification of Japanese Patent No. 3152151 is also not based on the physical characteristic. Thus, the abnormality detection has a problematic universality.